Effectiveness of Simulation-Based Transthoracic Echocardiography Training on Cardiac Sonography Students' Learning.
Bibliographic record
Abstract
Purpose: A primary challenge that cardiac sonography students experience during the echocardiography learning process is correlating between the anatomical structures of the heart to transducer created two-dimensional (2D) ultrasound images. Simulation-based training using a manikin may enhance learning, but evidence supporting its effectiveness in Transthoracic Echocardiography (TTE) education remains limited. This study evaluates the effectiveness of simulation-based versus traditional didactic teaching of cardiac anatomy and corresponding TTE images. Methods: A simulation-based learning lab was introduced in Fall 2023 within the Cardiac Anatomy TTE course at our institution. Thirty-five first-year cardiac sonography students (with no prior TTE training) completed the traditional didactic course, including pre- and post-course assessments, and participated in a hands-on simulation lab. The session included real-time scanning of a manikin for Parasternal Long Axis, Parasternal Short Axis, Apical 4 Chamber, and Apical 2 Chamber views, alongside an interactive three-dimensional (3D) cardiac anatomy review. The outcomes were compared to a control group (Spring 2023, n=42) that completed the didactic course without simulation. Results: Post-course assessment scores were significantly higher in the simulation cohort (p<0.0001), with an increase in mean scores from 72.12% (control) to 88.23% (simulation). The knowledge gain was more consistent in the simulation group (SD=3.26%) compared to the control (SD=13.38%). The lowest score in the simulation cohort was 76.47%, compared to 41.18% in the control. Conclusion: Simulation-based teaching significantly enhances knowledge acquisition and retention in cardiac anatomy TTE training of cardiac sonography students. Our findings support integrating structured simulation into echocardiography education to improve the learning process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".